Image segmentation is one of the most challenging problems faced by many medical imaging applications despite four decades of advance in the field. Segmentation methods should produce accurate, repeatable, and efficient segmentations of medical images which will lead to more effective visualization, manipulation and analysis of anatomic structures (objects). Segmentation consists of two tightly coupled tasks; recognition and delineation. Recognition is the process of identifying roughly the whereabouts of a particular object of interest and distinguishing it from other objects present in the image. Delineation is the process of specifying the precise spatial extent of the object. The processes of recognition and delineation cover a wide spectrum of segmentation approaches ranging from manual to automatic. Interactive approaches may be divided into three groups: image-based, model-based and hybrid. Image-based approaches utilize mostly information derived entirely from images to segment a given object. In model-based approaches, prior knowledge about the objects is incorporated into the model which drives the segmentation process. Hybrid approaches attempt to utilize the strengths of each of image- and model-based approaches to overcome weaknesses of the other.
The well known live wire approach is a very powerful interactive image-based segmentation method directly steered by the user. The live wire technique individually segments two dimensional medical image slices. Multiple slices make up a medical image data set for an object. Other popular two dimensional segmentation methods include the well known snakes computer program method (also known as active contour models) and Active Shape Models program (ASM). In the snakes computer program method, an energy functional based on contour deformation and external image forces is minimized. In the ASM approach, image searching is performed with a flexible and compact statistical shape model which is created by using prior knowledge derived from training data sets. Each of the three methods has strengths and weaknesses. It is desired to realize an approach that can improve the accuracy of a segmentation process while reducing the total amount of human interaction. The present invention addresses these concerns and others.